Subject Datasheet

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Budapest University of Technology and Economics
Faculty of Transportation Engineering and Vehicle Engineering
1. Subject name Automotive environment sensors
2. Subject name in Hungarian Járműipari környezetérzékelés
3. Code BMEKOKAM708 4. Evaluation type exam grade 5. Credits 5
6. Weekly contact hours 2 (28) Lecture 0 (0) Practice 2 (28) Lab
7. Curriculum
Autonomous Vehicle Control Engineering MSc (A)
 8. Role
Mandatory (mc) at Autonomous Vehicle Control Engineering MSc (A)
9. Working hours for fulfilling the requirements of the subject 150
Contact hours 56 Preparation for seminars 18 Homework 0
Reading written materials 20 Midterm preparation 20 Exam preparation 36
10. Department Department of Control for Transportation and Vehicle Systems
11. Responsible lecturer Dr. Bécsi Tamás
12. Lecturers Dr. Bécsi Tamás, Dr. Aradi Szilárd
13. Prerequisites  
14. Description of lectures
The perception of the environment and the understanding of the situation is of high importance for the development of modern driver assistance systems as well as for the development of autonomous vehicle systems. To do this, one has to know the physical background, possibilities and limitations of the existing environmental sensors.
The course aims the studying of the technologies developed for the tasks of environment sensing of an automated vehicle, the currently available technologies and the corresponding signal processing techniques.
First, the course introduces the inner sensors of the vehicles, such as position, velocity, translation or rotation, basics of their physical operation and their limitations. After this, the main principles of environment sensing, such as ultrasonic, radar, lidar and machine vision systems are introduced through application examples. To strengthen the robustness of the collected data, several typical sensor fusion techniques are also studied.
15. Description of practices
 
16. Description of labortory practices
The aim of the laboratory practice is to develop different measurements and software processing tasks.
17. Learning outcomes
A. Knowledge
  • is familiar with the sensors for measuring vehicle status, their operating principles
  • is familiar with the sensors and possibilities and limitations of environmental sensors used today (Radar, Lidar, Ultrasound, Camera Systems)
  • is familiar with the sensory fusion techniques used in environmental sensing
  • is familiar with the methods of processing the data of environmental sensors
B. Skills
  • can interpret the data of different sensors
  • is able to design an algorithm for simple determination of the environmental situation based on sensor data
  • is able to select an appropriate sensor architecture for the implementation of a designated driving support / autonomous vehicle function
C. Attitudes
  • is interested in the latest trends of automotive sensors
  • is interested in the algorithmization aspect of the sensor information processing tasks
D. Autonomy and Responsibility
  • being able to work in a team responsibly to design an autonomous vehicle function
18. Requirements, way to determine a grade (obtain a signature)
For signature: succesful fulfilment of two midterm exams. Final grade is the average of the two midterm tests (25-25%) and the exam (50%).
19. Opportunity for repeat/retake and delayed completion
One Midterm exam can be retried
20. Learning materials
Lecture Notes
Effective date 10 October 2019 This Subject Datasheet is valid for 2023/2024 semester II